Overview

Dataset statistics

Number of variables10
Number of observations6784
Missing cells248
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory530.1 KiB
Average record size in memory80.0 B

Variable types

DateTime1
Numeric8
Categorical1

Alerts

Close is highly overall correlated with High and 4 other fieldsHigh correlation
High is highly overall correlated with Close and 4 other fieldsHigh correlation
Low is highly overall correlated with Close and 4 other fieldsHigh correlation
MA_200 is highly overall correlated with Close and 4 other fieldsHigh correlation
MA_50 is highly overall correlated with Close and 4 other fieldsHigh correlation
Open is highly overall correlated with Close and 4 other fieldsHigh correlation
Stock Splits is highly imbalanced (99.5%)Imbalance
MA_200 has 199 (2.9%) missing valuesMissing
Date has unique valuesUnique
Dividends has 6731 (99.2%) zerosZeros

Reproduction

Analysis started2026-01-12 14:21:56.874290
Analysis finished2026-01-12 14:22:03.218988
Duration6.34 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Date
Date

Unique 

Distinct6784
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size53.1 KiB
Minimum1999-01-22 00:00:00-05:00
Maximum2026-01-09 00:00:00-05:00
Invalid dates0
Invalid dates (%)0.0%
2026-01-12T19:52:03.306006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:03.417593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Open
Real number (ℝ)

High correlation 

Distinct6767
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.723747
Minimum0.031993846
Maximum208.06841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.1 KiB
2026-01-12T19:52:03.509932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.031993846
5-th percentile0.085015067
Q10.26262534
median0.44860387
Q35.6154271
95-th percentile116.71684
Maximum208.06841
Range208.03642
Interquartile range (IQR)5.3528017

Descriptive statistics

Standard deviation36.023881
Coefficient of variation (CV)2.6249304
Kurtosis10.800644
Mean13.723747
Median Absolute Deviation (MAD)0.28800476
Skewness3.3685611
Sum93101.901
Variance1297.72
MonotonicityNot monotonic
2026-01-12T19:52:03.600567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.040589175652
 
< 0.1%
0.27963451632
 
< 0.1%
0.38186157062
 
< 0.1%
0.27734238532
 
< 0.1%
0.040589184292
 
< 0.1%
0.038320933072
 
< 0.1%
0.032471522692
 
< 0.1%
0.25594985132
 
< 0.1%
0.16373102082
 
< 0.1%
0.3025554032
 
< 0.1%
Other values (6757)6764
99.7%
ValueCountFrequency (%)
0.031993845681
< 0.1%
0.031993851761
< 0.1%
0.031993860371
< 0.1%
0.032232227941
< 0.1%
0.032232238171
< 0.1%
0.032351419331
< 0.1%
0.032471516711
< 0.1%
0.032471522692
< 0.1%
0.032471538091
< 0.1%
0.032590724531
< 0.1%
ValueCountFrequency (%)
208.06841461
< 0.1%
207.9684231
< 0.1%
206.43850021
< 0.1%
205.13857661
< 0.1%
202.98869741
< 0.1%
198.75894191
< 0.1%
196.40906651
< 0.1%
195.93908921
< 0.1%
195.70910641
< 0.1%
195.14913481
< 0.1%

High
Real number (ℝ)

High correlation 

Distinct6757
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.95342
Minimum0.032590711
Maximum212.1782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.1 KiB
2026-01-12T19:52:03.717448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.032590711
5-th percentile0.088239359
Q10.27021787
median0.45530724
Q35.6986254
95-th percentile118.50035
Maximum212.1782
Range212.1456
Interquartile range (IQR)5.4284075

Descriptive statistics

Standard deviation36.570085
Coefficient of variation (CV)2.6208689
Kurtosis10.699942
Mean13.95342
Median Absolute Deviation (MAD)0.29000979
Skewness3.3568973
Sum94660.002
Variance1337.3711
MonotonicityNot monotonic
2026-01-12T19:52:03.805602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28555539622
 
< 0.1%
0.30347222572
 
< 0.1%
0.31630786612
 
< 0.1%
0.33831205832
 
< 0.1%
0.038917790692
 
< 0.1%
0.042260560622
 
< 0.1%
0.28651078282
 
< 0.1%
0.27688409312
 
< 0.1%
0.037484780212
 
< 0.1%
0.042021272292
 
< 0.1%
Other values (6747)6764
99.7%
ValueCountFrequency (%)
0.032590710871
< 0.1%
0.032709892841
< 0.1%
0.032709898872
< 0.1%
0.032709900211
< 0.1%
0.032709903581
< 0.1%
0.032709905551
< 0.1%
0.032709914382
< 0.1%
0.032949190592
< 0.1%
0.032949192211
< 0.1%
0.032949206021
< 0.1%
ValueCountFrequency (%)
212.17819551
< 0.1%
211.32822761
< 0.1%
207.95841981
< 0.1%
206.14853021
< 0.1%
203.95864461
< 0.1%
203.13869131
< 0.1%
202.90870481
< 0.1%
199.9288751
< 0.1%
197.60899661
< 0.1%
195.98908941
< 0.1%

Low
Real number (ℝ)

High correlation 

Distinct6748
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.46518
Minimum0.030560839
Maximum205.54855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.1 KiB
2026-01-12T19:52:03.901950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.030560839
5-th percentile0.082856905
Q10.25518525
median0.44218142
Q35.4789335
95-th percentile113.95423
Maximum205.54855
Range205.51799
Interquartile range (IQR)5.2237483

Descriptive statistics

Standard deviation35.370388
Coefficient of variation (CV)2.626804
Kurtosis10.872854
Mean13.46518
Median Absolute Deviation (MAD)0.28558429
Skewness3.3765708
Sum91347.781
Variance1251.0644
MonotonicityNot monotonic
2026-01-12T19:52:04.003594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.038201743253
 
< 0.1%
0.26358988842
 
< 0.1%
0.036530353132
 
< 0.1%
0.27367508712
 
< 0.1%
0.032232243042
 
< 0.1%
0.032471522692
 
< 0.1%
0.032709899062
 
< 0.1%
0.29384537362
 
< 0.1%
0.031993845682
 
< 0.1%
0.03676872352
 
< 0.1%
Other values (6738)6763
99.7%
ValueCountFrequency (%)
0.030560839481
< 0.1%
0.031277805571
< 0.1%
0.031366736581
< 0.1%
0.031516180461
< 0.1%
0.031516184051
< 0.1%
0.031516186891
< 0.1%
0.031516189121
< 0.1%
0.031635367031
< 0.1%
0.031635368581
< 0.1%
0.03187465291
< 0.1%
ValueCountFrequency (%)
205.54855071
< 0.1%
204.76860411
< 0.1%
202.05875451
< 0.1%
201.39879451
< 0.1%
197.91897241
< 0.1%
194.63916081
< 0.1%
193.77920811
< 0.1%
191.89932641
< 0.1%
191.28934921
< 0.1%
191.11936551
< 0.1%

Close
Real number (ℝ)

High correlation 

Distinct5740
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.720324
Minimum0.031277806
Maximum207.02847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.1 KiB
2026-01-12T19:52:04.102088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.031277806
5-th percentile0.085033777
Q10.26307414
median0.44984381
Q35.5775133
95-th percentile116.35402
Maximum207.02847
Range206.9972
Interquartile range (IQR)5.3144392

Descriptive statistics

Standard deviation35.99214
Coefficient of variation (CV)2.6232718
Kurtosis10.764796
Mean13.720324
Median Absolute Deviation (MAD)0.28869874
Skewness3.3644958
Sum93078.679
Variance1295.4341
MonotonicityNot monotonic
2026-01-12T19:52:04.186158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.29797112947
 
0.1%
0.04011150457
 
0.1%
0.040827553726
 
0.1%
0.038440119486
 
0.1%
0.16380803296
 
0.1%
0.31699544195
 
0.1%
0.036291062835
 
0.1%
0.032471522695
 
0.1%
0.33579060445
 
0.1%
0.25877645615
 
0.1%
Other values (5730)6727
99.2%
ValueCountFrequency (%)
0.031277805572
 
< 0.1%
0.031516186891
 
< 0.1%
0.032232232392
 
< 0.1%
0.032351419333
< 0.1%
0.032471522695
0.1%
0.032590724533
< 0.1%
0.032709892842
 
< 0.1%
0.032949190594
0.1%
0.033187560743
< 0.1%
0.033247169111
 
< 0.1%
ValueCountFrequency (%)
207.02847291
< 0.1%
206.86848451
< 0.1%
202.87870791
< 0.1%
202.47872921
< 0.1%
201.01881411
< 0.1%
199.03892521
< 0.1%
198.67893981
< 0.1%
195.19914251
< 0.1%
193.78921511
< 0.1%
193.14924621
< 0.1%

Volume
Real number (ℝ)

Distinct6700
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8620415 × 108
Minimum19680000
Maximum9.230856 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.1 KiB
2026-01-12T19:52:04.283567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19680000
5-th percentile1.7098942 × 108
Q13.219975 × 108
median4.88559 × 108
Q37.189725 × 108
95-th percentile1.3148088 × 109
Maximum9.230856 × 109
Range9.211176 × 109
Interquartile range (IQR)3.96975 × 108

Descriptive statistics

Standard deviation4.2941819 × 108
Coefficient of variation (CV)0.73254034
Kurtosis37.754507
Mean5.8620415 × 108
Median Absolute Deviation (MAD)1.90113 × 108
Skewness3.82498
Sum3.9768089 × 1012
Variance1.8439998 × 1017
MonotonicityNot monotonic
2026-01-12T19:52:04.405372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4517280003
 
< 0.1%
5034480003
 
< 0.1%
3392160002
 
< 0.1%
3775560002
 
< 0.1%
2269440002
 
< 0.1%
3758880002
 
< 0.1%
5678160002
 
< 0.1%
4253720002
 
< 0.1%
3888000002
 
< 0.1%
3044240002
 
< 0.1%
Other values (6690)6762
99.7%
ValueCountFrequency (%)
196800001
< 0.1%
250080001
< 0.1%
284640001
< 0.1%
304320001
< 0.1%
310560001
< 0.1%
312960001
< 0.1%
333120001
< 0.1%
338400001
< 0.1%
356160001
< 0.1%
357600001
< 0.1%
ValueCountFrequency (%)
92308560001
< 0.1%
51354720001
< 0.1%
50889480001
< 0.1%
48371400001
< 0.1%
42928200001
< 0.1%
39885480001
< 0.1%
39308520001
< 0.1%
38490120001
< 0.1%
37989000001
< 0.1%
37675800001
< 0.1%

Dividends
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2967276 × 10-5
Minimum0
Maximum0.01
Zeros6731
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size53.1 KiB
2026-01-12T19:52:04.494911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.01
Range0.01
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00042689002
Coefficient of variation (CV)12.948902
Kurtosis336.29138
Mean3.2967276 × 10-5
Median Absolute Deviation (MAD)0
Skewness16.826456
Sum0.22365
Variance1.8223509 × 10-7
MonotonicityNot monotonic
2026-01-12T19:52:04.557299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
06731
99.2%
0.00422
 
0.3%
0.017
 
0.1%
0.0021256
 
0.1%
0.0018754
 
0.1%
0.003754
 
0.1%
0.0028754
 
0.1%
0.00354
 
0.1%
0.002452
 
< 0.1%
ValueCountFrequency (%)
06731
99.2%
0.0018754
 
0.1%
0.0021256
 
0.1%
0.002452
 
< 0.1%
0.0028754
 
0.1%
0.00354
 
0.1%
0.003754
 
0.1%
0.00422
 
0.3%
0.017
 
0.1%
ValueCountFrequency (%)
0.017
 
0.1%
0.00422
 
0.3%
0.003754
 
0.1%
0.00354
 
0.1%
0.0028754
 
0.1%
0.002452
 
< 0.1%
0.0021256
 
0.1%
0.0018754
 
0.1%
06731
99.2%

Stock Splits
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size344.6 KiB
0.0
6778 
2.0
 
3
1.5
 
1
4.0
 
1
10.0
 
1

Length

Max length4
Median length3
Mean length3.0001474
Min length3

Characters and Unicode

Total characters20353
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06778
99.9%
2.03
 
< 0.1%
1.51
 
< 0.1%
4.01
 
< 0.1%
10.01
 
< 0.1%

Length

2026-01-12T19:52:04.636612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-12T19:52:04.714704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.06778
99.9%
2.03
 
< 0.1%
1.51
 
< 0.1%
4.01
 
< 0.1%
10.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
013562
66.6%
.6784
33.3%
23
 
< 0.1%
12
 
< 0.1%
51
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)20353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013562
66.6%
.6784
33.3%
23
 
< 0.1%
12
 
< 0.1%
51
 
< 0.1%
41
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013562
66.6%
.6784
33.3%
23
 
< 0.1%
12
 
< 0.1%
51
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013562
66.6%
.6784
33.3%
23
 
< 0.1%
12
 
< 0.1%
51
 
< 0.1%
41
 
< 0.1%

MA_50
Real number (ℝ)

High correlation 

Distinct6733
Distinct (%)> 99.9%
Missing49
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean13.150097
Minimum0.03362085
Maximum187.29759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.1 KiB
2026-01-12T19:52:04.809062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.03362085
5-th percentile0.095040684
Q10.26599503
median0.44813194
Q35.6388593
95-th percentile115.94279
Maximum187.29759
Range187.26397
Interquartile range (IQR)5.3728643

Descriptive statistics

Standard deviation34.493015
Coefficient of variation (CV)2.6230235
Kurtosis11.049397
Mean13.150097
Median Absolute Deviation (MAD)0.28064128
Skewness3.3976801
Sum88565.906
Variance1189.7681
MonotonicityNot monotonic
2026-01-12T19:52:04.904489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18697004172
 
< 0.1%
0.26044671742
 
< 0.1%
186.99299471
 
< 0.1%
186.45002011
 
< 0.1%
0.038518964871
 
< 0.1%
186.36893981
 
< 0.1%
0.0390586141
 
< 0.1%
0.039046676311
 
< 0.1%
0.039018015861
 
< 0.1%
0.039068148431
 
< 0.1%
Other values (6723)6723
99.1%
(Missing)49
 
0.7%
ValueCountFrequency (%)
0.033620849921
< 0.1%
0.033632787091
< 0.1%
0.033651893881
< 0.1%
0.03367100061
< 0.1%
0.033687687141
< 0.1%
0.033756944611
< 0.1%
0.033783184811
< 0.1%
0.033861995561
< 0.1%
0.033864361571
< 0.1%
0.033916913791
< 0.1%
ValueCountFrequency (%)
187.29759311
< 0.1%
187.24180081
< 0.1%
187.23499081
< 0.1%
187.1158091
< 0.1%
187.08759251
< 0.1%
186.99299471
< 0.1%
186.88439611
< 0.1%
186.86459751
< 0.1%
186.84139861
< 0.1%
186.83861941
< 0.1%

MA_200
Real number (ℝ)

High correlation  Missing 

Distinct6584
Distinct (%)> 99.9%
Missing199
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean11.45926
Minimum0.039200333
Maximum162.17274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.1 KiB
2026-01-12T19:52:05.007210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.039200333
5-th percentile0.12822074
Q10.29043214
median0.42746328
Q35.2263715
95-th percentile91.309057
Maximum162.17274
Range162.13354
Interquartile range (IQR)4.9359394

Descriptive statistics

Standard deviation29.687081
Coefficient of variation (CV)2.5906629
Kurtosis11.043004
Mean11.45926
Median Absolute Deviation (MAD)0.26321712
Skewness3.417411
Sum75459.229
Variance881.32276
MonotonicityNot monotonic
2026-01-12T19:52:05.106334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.40190124422
 
< 0.1%
0.039737538581
 
< 0.1%
0.039852142911
 
< 0.1%
0.040009723721
 
< 0.1%
0.040176257451
 
< 0.1%
0.040334434271
 
< 0.1%
0.040554689911
 
< 0.1%
0.04078091421
 
< 0.1%
0.040985056551
 
< 0.1%
0.041179045031
 
< 0.1%
Other values (6574)6574
96.9%
(Missing)199
 
2.9%
ValueCountFrequency (%)
0.039200333411
< 0.1%
0.039282105811
< 0.1%
0.039351941091
< 0.1%
0.039433713561
< 0.1%
0.039513698161
< 0.1%
0.039613380881
< 0.1%
0.039737538581
< 0.1%
0.039852142911
< 0.1%
0.040009723721
< 0.1%
0.040176257451
< 0.1%
ValueCountFrequency (%)
162.17273941
< 0.1%
161.85177991
< 0.1%
161.53351981
< 0.1%
161.1763631
< 0.1%
160.83270541
< 0.1%
160.47959871
< 0.1%
160.1123941
< 0.1%
159.77743551
< 0.1%
159.44797511
< 0.1%
159.08467031
< 0.1%

Interactions

2026-01-12T19:52:02.178588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:57.491346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.082245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.788093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:59.402118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.188790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.925823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.549329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.258721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:57.570150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.148283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.851027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:59.473884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.255038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.006418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.622032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.338210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:57.643227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.217952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.917778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:59.547159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.419028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.076538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.697714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.417362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:57.716475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.288436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.992433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:59.658933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.523559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.152287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.775035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.489856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:57.785694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.352889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:59.062078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:59.860992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.601891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.229474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.855910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.568854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:57.855396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-12T19:51:59.929693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.676688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.302319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.932862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.785850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:57.927153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.608791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:59.253763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.013290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.760653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.383601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.002471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.866608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.005413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:58.702043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:51:59.330059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.097434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:00.836128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:01.465266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-12T19:52:02.096194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-12T19:52:05.185808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CloseDividendsHighLowMA_200MA_50OpenStock SplitsVolume
Close1.0000.0711.0001.0000.9490.9890.9990.031-0.274
Dividends0.0711.0000.0710.0720.0680.0700.0710.000-0.054
High1.0000.0711.0001.0000.9490.9891.0000.030-0.270
Low1.0000.0721.0001.0000.9490.9891.0000.029-0.280
MA_2000.9490.0680.9490.9491.0000.9700.9500.067-0.320
MA_500.9890.0700.9890.9890.9701.0000.9890.080-0.287
Open0.9990.0711.0001.0000.9500.9891.0000.029-0.275
Stock Splits0.0310.0000.0300.0290.0670.0800.0291.0000.005
Volume-0.274-0.054-0.270-0.280-0.320-0.287-0.2750.0051.000

Missing values

2026-01-12T19:52:02.987819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-12T19:52:03.068722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-12T19:52:03.165948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateOpenHighLowCloseVolumeDividendsStock SplitsMA_50MA_200
01999-01-22 00:00:00-05:000.0401120.0447670.0355750.03760527146880000.00.0NaNNaN
11999-01-25 00:00:00-05:000.0405890.0420210.0376050.0415455104800000.00.0NaNNaN
21999-01-26 00:00:00-05:000.0420210.0428570.0377240.0383213432000000.00.0NaNNaN
31999-01-27 00:00:00-05:000.0384400.0393950.0362910.0382022443680000.00.0NaNNaN
41999-01-28 00:00:00-05:000.0382020.0384400.0378430.0380822275200000.00.0NaNNaN
51999-01-29 00:00:00-05:000.0380820.0382020.0362910.0362912440320000.00.0NaNNaN
61999-02-01 00:00:00-05:000.0362910.0372460.0362910.0370081547040000.00.0NaNNaN
71999-02-02 00:00:00-05:000.0362910.0372460.0330680.0341432640960000.00.0NaNNaN
81999-02-03 00:00:00-05:000.0336650.0353370.0334260.034859751200000.00.0NaNNaN
91999-02-04 00:00:00-05:000.0353370.0377240.0348590.0367691819200000.00.0NaNNaN
DateOpenHighLowCloseVolumeDividendsStock SplitsMA_50MA_200
67742025-12-26 00:00:00-05:00189.919998192.690002188.000000190.5299991397403000.00.0186.058481159.084670
67752025-12-29 00:00:00-05:00187.710007188.759995185.910004188.2200011200061000.00.0186.186883159.447975
67762025-12-30 00:00:00-05:00188.240005188.990005186.929993187.539993976873000.00.0186.273487159.777435
67772025-12-31 00:00:00-05:00189.570007190.559998186.490005186.5000001201005000.00.0186.350890160.112394
67782026-01-02 00:00:00-05:00189.839996192.929993188.259995188.8500061482405000.00.0186.504892160.479599
67792026-01-05 00:00:00-05:00191.759995193.630005186.149994188.1199951835297000.00.0186.661892160.832705
67802026-01-06 00:00:00-05:00190.520004192.169998186.820007187.2400051768626000.00.0186.763695161.176363
67812026-01-07 00:00:00-05:00188.570007191.369995186.559998189.1100011535432000.00.0186.820903161.533520
67822026-01-08 00:00:00-05:00189.110001189.550003183.710007185.0399931724570000.00.0186.692116161.851780
67832026-01-09 00:00:00-05:00185.080002186.339996183.669998184.8600011310720000.00.0186.368940162.172739